In the presence of noisy labels, representation learning through feature clustering methods like those in [1]}, [2]}, [3]}, [4]} may compute corrupted class prototypes. In object detection, region features of different classes may be similar (such as the “Car" and “Truck" categories, see Figure REF ), rendering outlier removal methods ineffective in cases of mis-classification. In order to address this, we propose a transformer that utilizes self-attention to focus on salient regions-of-interest for prototype computation. A close-up view of the transformer model is shown in Figure REF .
